Please implement the latest examples in the examples/ folder to the docs/mkdocs.yml to see theses examples on the homepage. Perhaps a subsectioning for that many examples is necessary. See Homepage

Thank you!

I am attempting to run faceswap on an NVIDIA Jetson Nano board.

A few of the required faceswap dependencies didn't have pre-built binaries for this chip architecture so I manually compiled them, including:

• opencv-python==4.1.2.30
• numpy==1.17.4

The faceswap program runs ok and I can begin to train but it always eventuall

I was going though the existing enhancement issues again and though it'd be nice to collect ideas for spaCy plugins and related projects. There are always people in the community who are looking for new things to build, so here's some inspiration ✨ For existing plugins and projects, check out the spaCy universe.

If you have questions about the projects I suggested,

transcribe.py has odd directory-scanning behavior which isn't documented

If you point --src to a directory, you get the error:

E Path in --src not existing

Looking at the code logic, the script expects a JSON file with a .catalog file extension. This is (1) not documented, and (2) not a really useful logic. It would be much better to point the script to a dir, and scan f

Excuse me to write here, cant find another way to communicate with author.

Do you have a Russian manual sources? I'd like to make it (documentation) with Markdown, not PDF. I think it will be more github-ish way. You can contact me with solo12zw74 (at) yandex.ru.

Description:
This issue occurs when latest version of SciPy (1.2.0) is used while running this project.

Possible Cause:
Apparently, imread is deprecated and is moved to imageio.

Current Workaround:
Install SciPy version 1.1.0

Fix:
Can modify the code to use imageio.imread () instead of scipy.mis.imread()

The sonnet documentation states the about the Conv2DTranspose:

This acts as a light wrapper around the TensorFlow op tf.nn.conv2d_transpose
abstracting away variable creation and sharing.

tf.layers.conv2d_transpose(inputs, filters=64, kernel_size=1, strides=5, padding='valid')
--> yields shape (?, 5, 5, 64)

snt.Conv2DTranspose(64, 1, 5, padding='VALID')(inputs)
--> yiel

The documentation of the Yaml config parameters is missing an explanatory entry for the optional learning_rate_schedule property, described more here.

(Sorry, not a bug, but didn't find a b

Here, I don't understand why we need to multiply the input image by the label. Don't we need to concat the two part in CGAN? I'll be really appreciate if anyone can help me out!

Sphinx (2.2.1 or master) produces the following two kinds of warnings in my environment.

duplicate object description

I think cross refs to such object is ambiguous.

autosummary: stub file not found

There are

• chainer.dataset.Converter base class and
• chainer.dataset.converter decorator.

Therefore the filesystem has to allow to store `chainer.dataset.Conver

Sample on front page:

const model = new KerasJS.Model({
  filepaths: {

filepaths in plural.
code for Model:

if (!filepath) {
      throw new Error('[Model] path to protobuf-serialized model definition file is missing.')
    }

I think there is a little typo which gives the following error:
TypeError: init() got an unexpected keyword argument 'mu'

I checked the "mixture_gaussian_gibbs.py" example and I suggest the following suggestion
params = {'loc': tf.zeros(5), 'scale': tf.ones(5)}

https://github.com/blei-lab/edward/blob/352596c3bc77e1cb5c01f77ac3283a53c74dbf21/edward/models/param_mixture.py#L36

Hey Spandan,

Looks like something has changed in the data or model, the TF precision and recall in the final runs are very low (.2 or so) might need.to future proof this a bit more against changes to the TMDB or IMDB apis

While I was using Your library (it is great lib!), I found two strange things.
Sorry for my English.

1. In your documentation You write

# Create your new operation by inheriting from the Operation superclass:
class FoldImage(Operation):
    # Here you can accept as many custom parameters as required:
    def __init__(self, probability, num_of_folds):
        # Call the superclass's c

When passing a two-dimensional input layer to LSTMLayer, it will break with an uninterpretable error message:

>>> lasagne.layers.LSTMLayer((10, 20), 30)
[...]
TypeError: 'numpy.float64' object cannot be interpreted as an index

The reason is that np.prod(()) returns 1.0 as a numpy.float64 instance when computing num_units: https://github.com/Lasagne/Lasagne/blob/master/lasagne

The README.md doesn't indicate how to contribute. I made a local branch to fix issue #34 but when I try to push the branch I get the following error, which indicates I don't have permission.

Please indicate in the README how to contribute.

Thanks.